Update README.md

README.md

@@ -8,4 +8,23 @@ metrics:
 - accuracy
 - f1
 pipeline_tag: text-classification
----
+widget:
+  - text: We should lock the door and scream that curse word we know.
+    example_title: Anger Tweet
+  - text: Whoa! No way! We are not eating that!
+    example_title: Disgust Tweet
+  - text: I sure am glad you told me earthquakes are a myth, Joy; otherwise, I’d be terrified right now.
+    example_title: Fear Tweet
+  - text: All right, everyone, fresh start. We are gonna have a good day, which will turn into a good week, which will turn into a good year, which turns into a good life!
+    example_title: Joy Tweet
+  - text: Crying helps me slow down and obsess over the weight of life's problems.
+    example_title: Sadness Tweet
+---
+
+First posted on my [Kaggle](https://www.kaggle.com/code/wesleyacheng/twitter-emotion-multilabel-classification-w-bert/notebook#Create-Custom-Dataset).
+
+Hello, I'm Wesley, nice to meet you! 👋
+
+Since adding **Joy** and **Sadnesss** with **Anger** in my [Twitter Emotion MultiClass Classifier Notebook](https://www.kaggle.com/code/wesleyacheng/twitter-emotion-classification-with-bert), I wanted to complete the Inside Out group with **Fear** and **Disgust**!
+
+Here I made a Twitter Emotion MultiLabel Classifier by doing transfer learning on [BERT](https://huggingface.co/distilbert-base-uncased) with the [SemEval Twitter Dataset](https://huggingface.co/datasets/sem_eval_2018_task_1) in PyTorch and HuggingFace.